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Paperwork is the lifeblood of many organizations. According to a source, 15% of the company’s revenue is spent on creating, managing and distributing paper documents. But documents aren’t just expensive – they’re time consuming and full of mistakes. More than nine out of 10 employees responding to the 2021 ABBY survey stated that they spend eight hours each week searching for documents to find data, and that it takes an average of three hours to create a new document using the traditional method and six punctuation, spelling, Omission or printing.
Intelligent Document Processing (IDP) is considered as a solution to the problem of file management and orchestration. IDP combines technologies such as computer vision, optical character recognition (OCR), machine learning, and natural language processing to digitize paper and electronic documents and subsequently extract data – as well as analyze it. For example, IDP may validate information in files such as invoices through cross-referencing with databases, lexicons, and other digital data sources. Technology can also sort documents into separate storage buckets to keep them up-to-date and better organized.
Due to IDP’s potential to reduce costs and free up employees for more meaningful work, interest is growing. According to KBV research, the market for IDP solutions could reach $ 4.1 billion by 2027, with a compounded annual growth rate of 29.2% from 2021.
Processing documents with AI
Every industry and every company has an abundance of paper documents, no matter how enthusiastically the industry or company embraces digitization. Uses files for compliance, governance or organizational reasons, such as enterprise order tracking, records, purchase orders, statements, maintenance logs, employee onboarding, claims, proof of delivery and more.
A 2016 Wakefield research study found that 73% of “owners and decision makers” in companies with less than 500 employees print at least four times a day. As Randy Dezo, group director of Infotrends, explained to the CIO in a recent issue, employees use both printing and scanning for ad hoc business processes (for example, because it is more “in the moment” to scan receipts) and processes for “transactional” (e.g. Part of the daily workflow in the human resources, accounting and legal departments).
Adopting digitization alone does not solve every problem. In a 2021 study published by PandaDoc, 90% of companies that use digital files still find it difficult to create business proposals and HR documents.
The answer – or at least part of the answer – lies in the IDP. IDP automates the processing of data contained in documents, understanding what the document is about and understanding the information contained in it, extracting the information and sending it to the appropriate location.
The IDP platform starts with capturing data, often from several document types. The next step is the identification and classification of elements such as fields in the form, names of customers and businesses, phone numbers and signatures. Finally, the IDP platform validates and verifies data – either by rules, humans in a loop or both – before integrating it into a target system such as customer relationship management or enterprise resource planning software.
The two ways to identify data in IDP documents is OCR and handwritten-text identification. Technology that has been around for decades, OCR and handwritten text recognition attempts to capture key features in text, glyphs, and images, such as global features that describe the text as a whole and local features that describe individual parts of the text (e.g. That symmetrical characters).
Computer vision comes into play when it comes to images or the content inside images. Computer vision algorithms are “trained” to identify patterns by “seeing” the collection of data and, over time, the relationships between the pieces of data. For example, a basic computer vision algorithm can learn to distinguish cats from dogs by ingesting a large database of pictures of cats and dogs captioned “cat” and “dog”, respectively.
OCR, handwritten text recognition and computer vision are not without flaws. In particular, computer vision is sensitive to biases that can affect its accuracy. But the relative predictability of documents (e.g., invoices and barcodes follow a certain format) enables them to perform well in IDP.
Other algorithms handle post-processing steps such as brightening and removing artifacts such as ink stains and stains from files. For the understanding of text, it usually falls within the scope of Natural Language Processing (NLP). Like computer vision systems, NLP systems enhance their understanding of text by looking at many examples. Examples come in the form of documents in training datasets, ranging from terabytes of scraped data from social media, Wikipedia, books, software hosting platforms such as GitHub and other sources on the public web.
NLP-powered document processing allows employees to find key text in documents or publish trends and changes in documents over time. Depending on how the technology is implemented, the IDP platform can cluster the onboarding forms together in a folder or automatically paste the payroll information into the relevant tax PDF.
The final phase of IDP may include robotic process automation (RPA), a technology that automates tasks traditionally performed by humans using software robots that interact with enterprise systems. These AI-powered robots can handle a wide range of tasks, from moving files from database to database, copying text from documents, pasting them into email, and sending messages.
With RPA, the company can, for example, automate report creation by pulling software robots from various processed documents. Or they can remove duplicate entries in spreadsheets in various file formats and programs.
Rising IDP platform
Lured by the huge addressable market, a growing number of vendors are offering IDP solutions. While not all adopt the same approach, they share the goal of eliminating filing that would otherwise be done by humans.
Rosam, for example, provides an IDP platform called “Spatial OCR (Optical Character Recognition)” to extract data while making improvements. The platform essentially learns to recognize different compositions and patterns of different documents, such as the fact that one invoice number may be in the upper left in one invoice but somewhere else in the other.
Other IDP vendors focus on ZUVA, contract and document review, offering a trained model out of the box that can extract data points and present them in a question-and-answer format. M-Files applies algorithms to the metadata of documents to create a structure, integrating the categories and keywords used in a company. Indico, meanwhile, ingests documents and does post-processing with models that can classify and compare text as well as find feelings and phrases.
Among the tech giants, Microsoft is using IDP to gain knowledge from the organization’s emails, messages and documents from payments in the knowledge base. Amazon Web Services’ text service can recognize scans, PDFs and photos, and feed any extracted data to another system. For its part, Google hosts DocAI, a collection of tools available through AI-powered document parsers and APIs.
How IDP makes a difference
Forty-two percent of knowledge workers say paper-based workflows make their daily tasks less efficient, more expensive and less productive, according to IDC. And Foxit Software reports that more than two-thirds of companies acknowledge that their need for paperless office processes has increased during the epidemic.
The benefits of IDP cannot be overstated. But its implementation is not always easy. As KPMG analysts point out in a report, companies run the risk of not defining a clear strategy or efficient business goal, failing to keep humans in the loop, and misjudging the technical feasibility of IDP. Enterprises operating in highly regulated industries may also need to take additional security measures or precautions when using the IDP platform.
Still, technology promises to change the way companies do business – while saving money in the process. Lewis Walker of Deloitte writes, “Semi-structured and unstructured documents can now be automated faster and with greater accuracy, leading to more satisfied customers.” “In order to gain a competitive advantage in the automation-first age, business leaders scale, they will need to unlock higher value opportunities by processing documents more efficiently and turning that information into deeper insights faster than ever before.”
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